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Climate change is intensifying the hydrologic cycle and altering ecosystem function, including water flux to the atmosphere through evapotranspiration (ET). ET is made up of evaporation (E) via non‐stomatal surfaces, and transpiration (T) through plant stomata which are impacted by global changes in different ways. E and T are difficult to measure independently at the ecosystem scale, especially across multiple sites that represent different land use and land management strategies. To address this gap in understanding, we applied flux variance similarity (FVS) to quantify how E and T differ across 13 different ecosystems measured using eddy covariance in a 10 × 10 km area from the CHEESEHEAD19 experiment in northern Wisconsin, USA. The study sites included eight forests with a large deciduous broadleaf component, three evergreen needleleaf forests, and two wetlands. Average T/ET for the study period averaged nearly 52% in forested sites and 45% in wetlands, with larger values after excluding periods following rain events when evaporation from canopy interception may be expected. A dominance analysis revealed that environmental variables explained on average 69% of the variance of half‐hourly T, which decreased from summer to autumn. Deciduous and evergreen forests showed similar E trajectories over time despite differences in vegetation phenology, and vapor pressure deficit explained some 13% of the variance E in wetlands but only 5% or less in forests. Retrieval of E and T within a dense network of flux towers lends confidence that FVS is a promising approach for comparing ecosystem hydrology across multiple sites to improve our process‐based understanding of ecosystem water fluxes.

 

Nature-based Climate Solutions (NbCS) are managed alterations to ecosystems designed to increase carbon sequestration or reduce greenhouse gas emissions. While they have growing public and private support, the realizable benefits and unintended consequences of NbCS are not well understood. At regional scales where policy decisions are often made, NbCS benefits are estimated from soil and tree survey data that can miss important carbon sources and sinks within an ecosystem, and do not reveal the biophysical impacts of NbCS for local water and energy cycles. The only direct observations of ecosystem-scale carbon fluxes, e.g., by eddy covariance flux towers, have not yet been systematically assessed for what they can tell us about NbCS potentials, and state-of-the-art remote sensing products and land-surface models are not yet being widely used to inform NbCS policy making or implementation. As a result, there is a critical mismatch between the point- and tree- scale data most often used to assess NbCS benefits and impacts, the ecosystem and landscape scales where NbCS projects are implemented, and the regional to continental scales most relevant to policy making. Here, we propose a research agenda to confront these gaps using data and tools that have long been used to understand the mechanisms driving ecosystem carbon and energy cycling, but have not yet been widely applied to NbCS. We outline steps for creating robust NbCS assessments at both local to regional scales that are informed by ecosystem-scale observations, and which consider concurrent biophysical impacts, future climate feedbacks, and the need for equitable and inclusive NbCS implementation strategies. We contend that these research goals can largely be accomplished by shifting the scales at which pre-existing tools are applied and blended together, although we also highlight some opportunities for more radical shifts in approach. 

Understanding plant phenological change is of great concern in the context of global climate change. Phenological models can aid in understanding and predicting growing season changes and can be parameterized with gross primary production (GPP) estimated using the eddy covariance (EC) technique. This study used nine years of EC-derived GPP data from three mature subtropical longleaf pine forests in the southeastern United States with differing soil water holding capacity in combination with site-specific micrometeorological data to parameterize a photosynthesis-based phenological model. We evaluated how weather conditions and prescribed fire led to variation in the ecosystem phenological processes. The results suggest that soil water availability had an effect on phenology, and greater soil water availability was associated with a longer growing season (LOS). We also observed that prescribed fire, a common forest management activity in the region, had a limited impact on phenological processes. Dormant season fire had no significant effect on phenological processes by site, but we observed differences in the start of the growing season (SOS) between fire and non-fire years. Fire delayed SOS by 10 d ± 5 d (SE), and this effect was greater with higher soil water availability, extending SOS by 18 d on average. Fire was also associated with increased sensitivity of spring phenology to radiation and air temperature. We found that interannual climate change and periodic weather anomalies (flood, short-term drought, and long-term drought), controlled annual ecosystem phenological processes more than prescribed fire. When water availability increased following short-term summer drought, the growing season was extended. With future climate change, subtropical areas of the Southeastern US are expected to experience more frequent short-term droughts, which could shorten the region’s growing season and lead to a reduction in the longleaf pine ecosystem’s carbon sequestration capacity. 

Livestock agriculture accounts for ∼15% of global anthropogenic greenhouse gas (GHG) emissions. Recently, natural climate solutions (NCS) have been identified to mitigate farm‐scale GHG emissions. Nevertheless, their impacts are difficult to quantify due to farm spatial heterogeneity and effort required to measure changes in carbon stocks. Remote sensing (RS) models are difficult to parameterize for heterogeneous agricultural landscapes. Eddy covariance (EC) in combination with novel techniques that quantitatively match source area variations could help update such vegetation‐specific parameters while accounting for pronounced heterogeneity. We evaluate a plant physiological parameter, the maximum quantum yield (MQY), which is commonly used to calculate gross and net primary productivity in RS applications. RS models often rely on spatially invariable MQY, which leads to inconsistencies between RS and EC models. We evaluate if EC data improve RS models by updating crop specific MQYs to quantify agricultural GHG mitigation potentials. We assessed how farm harvest compared to annual sums of (a) RS without improvements, (b) EC results, and (c) EC‐RS models. We then estimated emissions to calculate the annual GHG balance, including mitigation through plant carbon uptake. Our results indicate that EC‐RS models significantly improved the prediction of crop yields. The EC model captures diurnal variations in carbon dynamics in contrast to RS models based on input limitations. A net zero GHG balance indicated that perennial vegetation mitigated over 60% of emissions while comprising 40% of the landscape. We conclude that the combination of RS and EC can improve the quantification of NCS in agroecosystems.

 

Abstract. Global ecosystems vary in their function, and therefore resilience to disturbance, as a result of their location on Earth, structure, and anthropogenic legacy. Resilience can therefore be difficult to describe solely based on energy partitioning, as it fails to effectively describe how ecosystems use available resources, such as soil moisture. Maximum entropy production (MEP) has been shown to be a better metric to describe these differences as it relates energy use efficiencies of ecosystems to the availability of resources. We studied three sites in a longleaf pine ecosystem with varying levels of anthropogenic legacy and biodiversity, all of which were exposed to extreme drought. We quantified their resilience from radiative, metabolic and overall MEP ratios. Sites with anthropogenic legacy had ~10% lower overall and metabolic energy use efficiency compared to more biodiverse sites. This resulted in lower resilience and a delay in recovery from drought by ~1 year. Additionally, a set of entropy ratios to determine metabolic and overall energy use efficiency explained more clearly site-specific ecosystem function, whereas the radiative entropy budget gave more insights about structural complexities at the sites. Our study provides foundational evidence of how MEP can be used to determine resiliency across ecosystems globally.

 

Abstract. Ecosystems are open systems that exchange matter and energy with theirenvironment. They differ in their efficiency in doing so as a result of theirlocation on Earth, structure and disturbance, including anthropogenic legacy.Entropy has been proposed to be an effective metric to describe thesedifferences as it relates energy use efficiencies of ecosystems to theirthermodynamic environment (i.e., temperature) but has rarely been studied tounderstand how ecosystems with different disturbance legacies respond whenconfronted with environmental variability. We studied three sites in alongleaf pine ecosystem with varying levels of anthropogenic legacy and plantfunctional diversity, all of which were exposed to extreme drought. Wequantified radiative (eff_rad), metabolic and overall entropychanges – as well as changes in exported to imported entropy(eff_flux) in response to drought disturbance and environmentalvariability using 24 total years of eddy covariance data (8 years per site).We show that structural and functional characteristics contribute todifferences in energy use efficiencies at the three study sites. Our resultsdemonstrate that ecosystem function during drought is modulated by decreasedabsorbed solar energy and variation in the partitioning of energy and entropyexports owing to differences in site enhanced vegetation index and/or soilwater content. Low eff_rad and metabolic entropy as well as slowadjustment of eff_flux at the anthropogenically altered siteprolonged its recovery from drought by approximately 1 year. In contrast,stands with greater plant functional diversity (i.e., the ones that includedboth C₃ and C₄ species) adjusted their entropy exports when facedwith drought, which accelerated their recovery. Our study provides a pathforward for using entropy to determine ecosystem function across differentglobal ecosystems.